Bias scoring of machine learning project data

ABSTRACT

Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to bias scoring of machine learningproject data.

BACKGROUND

Machine learning processes can potentially introduce a degree ofunintentional bias into data and models. The potential bias could gounrecognized and could go uncorrected.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 to detect and reduce potential machine learning bias inaccordance with various aspects described herein.

FIG. 2B depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2C depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2D depicts an illustrative embodiment of a system in accordancewith various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for detecting and reducing potential bias or unfairness inmachine learning systems. Other embodiments are described in the subjectdisclosure. It should be understood that, because the systems andmethods in this disclosure are meant to address potential outcomes andmitigate possible risks, any reference to bias means “potential bias”and is not a conclusion of bias in the common or legal sense of theword.

One or more aspects of the subject disclosure may include a deviceincluding a processing system including a processor and a memory thatstores executable instructions. The instructions, when executed by theprocessing system, facilitate performance of certain operations. Theoperations may include receiving, by the processing system, project datadefining a proposed project of an entity and storing the project data ina project database with other project data for other projects. Theoperations may further include extracting features of the proposedproject and, based on the extracted features, determining a clusteringassignment for the proposed project. Determining the clusteringassignment may comprise comparing information about the proposed projectincluding the extracted features with information about the otherprojects and assigning the proposed project to a cluster including oneor more projects having similar potential bias characteristics as theproposed project. The operations may further include determining a riskof bias for the proposed project and, based on the risk of bias,recommending a corrective action to reduce the risk of bias.

One or more aspects of the subject disclosure include a method that maycomprise steps of receiving, by a processing system including aprocessor, proposed project data for a proposed project, whereinreceiving proposed project data comprises receiving machine learning(ML) data, and wherein the proposed project is an ML project; andstoring the proposed project data in a project database which storesother project data for a plurality of other projects, wherein the otherprojects are ML projects. The method may comprise steps of extractingfeatures and metadata of the proposed project data and providing thefeatures and metadata of the proposed project data to a clusteringmodel, wherein the clustering model comprises an ML model. The methodmay comprise steps of receiving a clustering assignment for the proposedproject from the clustering model and providing the clusteringassignment and the features and the metadata of the proposed project toa potential bias scoring model, wherein the potential bias scoring modelcomprises an ML model. The method may comprise steps of receiving fromthe bias scoring model an indication of risk of potential bias for theproposed project and providing a recommendation for reducing any risk ofpotential bias for the proposed project. Providing the recommendationfor reducing a risk of potential bias for the proposed project mayinclude one or more of: providing a bias risk score, providing a list ofbias factors affecting the bias risk score where respective bias factorson that list may be adjusted to reduce the risk of potential bias fromthose previously identified as having similar potential biascharacteristic of the proposed project.

One or more aspects of the subject disclosure include a machine-readablemedium storing executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations. The operations may include receiving project data defining aproposed project of an entity and storing the project data in a projectdatabase with other project data for other projects. The operations mayfurther include extracting features of the proposed project from theproject data, including extracting keywords from a textual projectdescription of the proposed project, and retrieving metadata of theproposed project. The operations may further include determining aclustering assignment for the proposed project, including applying atleast some of the extracted features and at least some of the metadataof the proposed project to a clustering model to identify a cluster of aplurality of clusters of projects, wherein the identified clusterincludes projects similar to the proposed project. The operations mayfurther include providing the clustering assignment and at least some ofthe extracted features and at least some of the metadata of the proposedproject to a bias scoring model and receiving from the bias scoringmodel an indication of risk of potential bias for the proposed project.The operations may further include providing, based on the indication ofpotential bias, a recommendation for reducing the risk of potential biasfor the proposed project. Providing a recommendation for reducing therisk of potential bias may include providing a bias risk score andproviding a list of bias factors affecting the bias risk score.Respective bias factors of the list of bias factors may be adjusted toreduce the risk of potential bias for the proposed project.

In accordance with some aspects of this disclosure, given a database ofmachine learning projects, some embodiments permit creation of anontology of potential biases based on the project descriptors for theprojects. Bias may refer to an unintentional skew in population or adifference between expectation of a characteristic or population and atrue value of the characteristic or population. An ontology may be astructure of concepts or entities within a domain, organized byrelationships. Information about past and present projects is stored ina database. Information including textual descriptions of one or moreprojects is retrieved from the database. A keyword generating algorithmis then utilized on the project descriptions. The keyword generatingalgorithm extracts relevant and context-aware terms from the projectdescriptions. These extracted terms are coupled with other projectmetadata which are then used to cluster projects in the database intogroups having similar potential bias characteristics. The potential biasontology is then used to assign a bias-score to each of the clusters.When a new project is identified, one or more cluster memberships areassigned to the new project based on initial descriptions and relatedmetadata. The cluster bias scores are then used to provide a potentialbias score/categorization to the new project. As projects continue to beadded to the database, the reliability and robustness of the clusters,as well as the ontology that generated them, improve owing to iterativetraining.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example,communications network 100 can facilitate in whole or in part indetermining a potential risk for introduction of potential bias in amachine learning project, for example using machine learning models tocluster projects and score the machine learning project for potentialbias. In particular, a communications network 125 is presented forproviding broadband access 110 to a plurality of data terminals 114 viaaccess terminal 112, wireless access 120 to a plurality of mobiledevices 124 and vehicle 126 via base station or access point 122, voiceaccess 130 to a plurality of telephony devices 134, via switching device132 and/or media access 140 to a plurality of audio/video displaydevices 144 via media terminal 142. In addition, communication network125 is coupled to one or more content sources 175 of audio, video,graphics, text and/or other media. While broadband access 110, wirelessaccess 120, voice access 130 and media access 140 are shown separately,one or more of these forms of access can be combined to provide multipleaccess services to a single client device (e.g., mobile devices 124 canreceive media content via media terminal 142, data terminal 114 can beprovided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a method 200 that may be implemented by or function withinthe communication network of FIG. 1 in accordance with various aspectsdescribed herein. The method 200 is one exemplary embodiment of a methodor system for detecting and mitigating unintentional potential bias in amachine learning system.

In particular embodiments, aspects described herein may be implementedusing machine learning. For example, certain inputs to a system ormethod comprise data about projects that may have been developed bymachine learning. The data received as inputs may be further processedby machine learning systems to make decisions, draw inferences andprovide recommendations. Broadly, machine learning is a set of tools andprocedures of data analysis that automate analytical model building.Machine learning is an aspect of artificial intelligence. Machinelearning makes use of the idea that automated systems, implemented oncomputing equipment, can learn from data, identify patterns and makedecisions with minimal human intervention. The result is useful tohumans for making decisions and implementing further processes andsystems. Machine learning processes provide benefits of insight andinference that may be invisible to human review. But machine learningsystems risk introducing or masking bias that is inadvertently andunknowingly present.

In the present context, bias may refer to an unintentional skew orvariation in an actual population from what would be expected orintended for the population, or a difference between expectation of acharacteristic of a population on the one hand, and a true value of thecharacteristic of a population. Different types of potential biases maybe introduced into data or models for machine learning. For example,potential bias may be demographic in nature or geographic in nature.Potential bias may be behavioral or psychographic in nature. Other typesof potential bias may exist as well. Potential bias may be tied toaspects of a product or service or a function. For example, a project tooffer a new service may show potential bias by unintentionally beingdirected to or away from potential customers within certain demographicgroups or geographic areas. The targeted audience may be skewed towardor away from potential customers having a particular characteristic.Typically, the potential bias is unintentionally introduced based onunrecognized or hidden factors. It should be understood that, becausethe systems and methods in this disclosure are meant to addresspotential outcomes and mitigate possible risks, any reference to biasmeans “potential bias” and is not a conclusion of bias in the common orlegal sense of the word. Some research has been conducted on thedetection and mitigation of potential bias within a machine learningmodel, starting from the data or the model itself or the output of themodel. However, for an individual or a team working within anorganization, there is no current way to determine if a project is atrisk of exhibiting bias.

Potential bias may be related to demographic information, the nature ofdata being generated, processed and stored, or any item that could causeharm to an entity such as a business or an institution, products, abrand or a public reputation or perception. In an example, where abusiness is developing a new product or service, the business may usemachine learning to predict which current and future customers shouldreceive the new offering, if the result of the machine learning processcauses the offering to include or omit, for example, certain demographicor geographic groups, that could be construed as potential bias. Inanother example, suppose an employer implemented a machine learningprocess that relied on natural language processing to automaticallyreview received resumes from job seekers and tried to predict which jobseekers should be contacted for in-person interviews. This may be done,in part, by automatically comparing incoming resumes with a seed dataset of resumes from existing employees. However, any previousunintentional hiring bias that may be present in the existing workforcecould potentially be automatically duplicated by the natural languageprocess that matched the incoming resume characteristics to the existingworkforce. In this way, potential bias could be introduced by theprediction because the machine learning program does not understand orrecognize the historical context of the existing data.

From the perspective of the business, such potential bias is highlyundesirable. Potential bias may cause the business to develop the wrongproduct or service, or market the new product or service to anunintended group. If the product or service is targeted to theunintended group, the group or market segment is less likely to adoptthe product or process. Further, as noted, any potential bias may causereputational harm to the business and may introduce legal and otherrisks. Accordingly, detecting, identifying, correcting and avoidingpotential bias in any form is an important business and technical goal.Machine learning systems may be useful for achieving these goals;however, the subject disclosure contemplates systems that achieve suchgoals that may not be reliant on machine learning.

Current academic literature on potential bias detection and mitigationin machine learning (ML) processes focuses on where in the ML pipelinesuch potential bias may occur and developing algorithms to optimize afairness metric. There is a significant challenge, however, for projectmanagers and developers unfamiliar with this space in identifying whichprojects may pose a risk of potential bias at the outset. Even for MLexperts, as a new project is commissioned, it might be challenging yetnecessary to get a preliminary understanding of whether a developmentteam needs to be careful about any potential bias issues. As noted,currently there are no solutions for predicting whether a project issusceptible to potential bias issues at the outset.

In some aspects, the present disclosure provides for using machinelearning itself as a way of making predictions on which new projects arelikely to face a potential bias problem. The system produces a numericalindication of risk of potential bias in an ML project. In someembodiments, recommendations for mitigating or eliminating the potentialunintentional bias are provided. The disclosed embodiments allowdetection of potential bias early in the ML pipeline to permit anydetected potential bias to be mitigated.

Any entity or organization that deploys ML-based decision systems mayhave an internal database of plain-text, abstract-level descriptions ofML projects and systems. These abstracts may contain information on oneor more of the following: a description of the project, intended usecases, information on data sources, the ML tools used, final resultsincluding successes and failures, and names of contributors to theproject.

In accordance with various aspects described herein, a machine learningapproach is presented to automate categorization of each project'spotential for bias to improve opportunities for potential bias detectionand appropriate interventions in the machine learning cycle. Potentialbias may be categorized according to a range of risk, such as low riskfor bias, medium risk for bias, and high risk for bias introduction. Anumerical value may be determined, such as a number from 0.0 to 1.0where 0.0 represents no risk of bias and the risk of bias in the projectincreases with the numerical value. In some aspects, features extractedfrom ML projects and the resulting bias categorization information arestored in a machine learning database (MLDB).

Further in accordance with various aspects described herein, given adatabase of machine learning projects including project descriptions,keywords are extracted from the project descriptions. One viable way toperform keyword extraction is the TextRank algorithm which can providerelevant and context-aware terms. An ontology of potential bias is builtusing available information available, including project keywords, datasources, project contributors, end goals, etc. This ontology can berepresented as a clustering framework including a set of clusters ofrelated projects. Each cluster is assigned a bias score, indicating itspropensity to exhibit potential bias. When a new project is processed,its keywords are extracted from the project description and coupled withits related metadata. These keywords and metadata may be referred to asthe project's features. The project's features are fed through thebias-ontology to assign cluster memberships to the project. Based on theproject's cluster memberships, a bias score/categorization (e.g., lowrisk for bias, medium risk for bias, or high risk for bias) is assignedto the project. Over time, as ML projects continue to be added to thedatabase of projects, the bias-ontology or model is updated using thenew projects and the bias scores assigned to each of the clusters arere-computed. This continuous learning keeps the system up to date.

In accordance with some embodiments, a system and method to detect,score and compensate for potential bias in projects may include some orall of the following. First, given a repository of project descriptionsincluding abstracts, keywords and other metadata are extracted fromthose project descriptions. An example process for extracting keywordsis a TextRank algorithm. Second, projects are clustered into relatedcategories of projects. In some embodiments, an ontology of keywords andtheir co-occurrence, related to potential bias, as well as availableinformation on data sources and project contributors, end goals, etc.,may be used to cluster the projects. A risk of bias can then be inferredfrom each cluster. Third, a probability score that connotes a project'srisk of bias is generated or determined from each cluster. Theprobability score corresponds to the probability that, in some aspect,the project reveals some potential bias based on comparison with otherprojects. A project's bias potential may be augmented with informationon the project's data sources and contributors. Fourth, for a newincoming project, a cluster membership is assigned to the project basedon its descriptions and a corresponding risk score is assigned. The newproject may then be evaluated and modified according to the potentialfor bias. Recommendations for mitigation of potential bias may beprovided.

The disclosed embodiments provide substantial benefits for anorganization developing a number of projects over time. Informationabout past and present projects is collected and stored, including earlyversions of projects used for development or training, and incompleteprojects that were started but not completed or fully implemented. Wheresome potential bias is identified or otherwise detected or determined,the existence of some potential bias may be used to detect, identify andeliminate potential bias from current and future projects to improveperformance of the current and future projects and of the organization.

The disclosed embodiments provide substantial technical benefits andimprovements over conventional technology. The project partitioningprocess, assigning projects to clusters, can be done offline, meaningthat requirements for real-time computational and temporal efficiencyare reduced. Embodiments of the system and method produce, as an output,a bias risk classification. This information comprises a probabilityrather than a binary value. Such information has an implicit confidencelevel across a spectrum of confidence, rather than just a binary,up-down indication. This provides a project manager with the ability andauthority to set a particular threshold clearly identifying when concernover project bias is warranted. Ultimately, continuous improvement tothe clustering algorithm, as each new project is analyzed and added tothe model, is an inherent benefit as the database of projectdescriptions grows.

FIG. 2A depicts an illustrative embodiment of a method 200 in accordancewith various aspects described herein. FIG. 2A illustrates exemplarysteps of a process for implementing machine learning bias detection andclassification. The method 200 may be implemented as described herein,using conventional or specialized data processing equipment, such as adata processing system including one or more processors, memory such asa database storing data and instructions for operation of the processingsystem. The instructions, when executed by the processing system,facilitate performance of operations including operations forming method200 and other embodiments and variations. Generally, in someembodiments, the method 200 includes a feature extraction process 210, aclustering assignment process 220, a clustering model 222, an updateclustering model process 224, a bias scoring model 226, a recommendationmodule 225 and a bias correction module 235.

In the particular illustrated embodiment, an input to the method 200 isa project 202, labelled Example 2 in FIG. 2A. The project 202 may be anysuitable effort or initiative, undertaken on behalf of an entity ororganization or department thereof, or a group of entities operatingjointly, or by an individual. The project 202 may include inputs of itsown such as market studies, collected data and other information,business plans, information about identified risks and possible rewards,targeted markets and market segments, pre-launch plans, post-launchplans, short-term plans, long-term plans, etc. The project 202 may be anew telecommunications service offering, or a new device to be developedand sold, as non-limiting examples. The nature of the project 202 willtend to dictate the information represented by the project 202. Theproject 202 in some embodiments is a new project or a proposed projectto be analyzed for any potential bias that may be inadvertentlyintroduced by the project 202.

The project 202 may be characterized or defined in any suitable way. Insome embodiments, the project is characterized by a business unitassociated with the project, a project manager responsible for theproject and/or a textual description of the project. The business unitmay be a department or a cost center or a geographical operating unit ofa business, or any other identifiable organizational entity. The productmanager may be an individual or a group of individuals with managerialresponsibility for the project 202. In one example, the project 202involves launching a new telecommunications, media, internet oradvertising service, by a service provider, to a market segment ofcustomers, some of whom are existing customers of the service providerand some of whom will be new customers of the service provider for thenew service. However, the project can be associated with other services,products or tasks, which may or may not be associated withtelecommunications. In other examples, the offering could be a modifiedversion of an existing service or an improvement to an existing service,such as a service or product offered in a different language or adifferent format or through a different delivery channel (such as viabroadcast or cable television in addition to via broadband networks).

At block 204, the method determines if the project 202 currently existsin a project database. The project 202 in this embodiment ischaracterized by a variety of data stored in various sources. The datamay take any format and represent any relevant information. In oneexample, the data includes a high-level textual description of theexemplary telecommunication service offering, along with business goalsand input parameters.

At block 206, if the project 202 is not currently existent in theproject database, the project is added to project database 208. Projectdatabase 208 in some embodiments stores data for a wide range ofprojects. The stored projects may include projects previously undertakenby the business implementing the method and for which there ishistorical data. The stored projects may include projects started butnot actually implemented or early versions of projects that were startedand subsequently revised. The stored projects may include projects thatoriginated outside the business and for which data is available forsubsequent use and processing. At least some of the projects may show orotherwise be determined to show some evidence or characteristics ofpotential bias and may be labelled as such. In some cases, none of theprojects may have exhibited any potential bias and are thus notlabelled. Even if bias is not found in a project, benefits may beprovided to the business implementing the method. One of the mostvaluable things for a manager of a new project is to learn that theproject does not have bias concerns. The manager and team can then goabout building and deploying the project. The data of the project 202 isadded to the database 208. Any useful data formatting, abstraction,indexing or other processes may be performed at this time to facilitatesubsequent processing of the data forming the project 202.

If the project 202 exists in the database 208, or after the project hasbeen added to the database 208, a bias identification process isinitiated for the project. Initially, a feature extraction process 210operates to identify and extract features of the project 202 stored inthe database 208. Any suitable or available features may be identifiedand extracted. Examples in some embodiments include identifying, step212, the business unit associated with the project 202; identifying theproject manager, step 214, responsible for the project 202; identifying,step 216, a description of the project 202, or a title of the project202, or metadata for the project 202. As indicated by step 217, otherfeatures and information may be extracted by the feature extractionprocess 210 as well. For example, other project data that may beidentified and extracted include project team member identification,project use cases, and datasets being used by machine learning projects,and machine learning models being used, etc. Any data associated with aproject can be used to feed the clustering model 222.

The project description can vary in length for different projects. Toprocess the project description, step 216 may use natural languageprocessing techniques. In some embodiments, step 218 implements keywordextraction. One example of a suitable algorithm for extracting keywordsor key phrases is referred to as TextRank. Another suitable keywordextraction algorithm is term frequency—inverse document frequency(TF-IDF). In an example, TF-IDF generates weighting factors based on ananalysis of each individual word within each project description, andhow frequently each word occurs within each project description as wellas word frequency across all project descriptions in the projectdatabase 208.

Other examples of keyword or key phrase extraction may be readilyimagined. Any other suitable algorithm, process or tool may be used.Once keywords are extracted, other less relevant words are filtered out.The result may be provided to a clustering assignment process 220.

Referring to the clustering model 222, in some embodiments, theclustering model 222 is trained offline using contents of the projectdatabase 208. The clustering assignment process 220 receives informationabout the project description including extracted keywords and projectmetadata and provides the received information to the clustering model222 via an update clustering model process 224. The clusteringassignment process 220 determines from the output of the clusteringmodel 222 which cluster the new project 202 belongs to. Subsequently,the update clustering model process 224 is performed, creating afeedback loop so that the process always has the most relevant andup-to-date model.

Further, the result of the clustering assignment process 220 is passedto a bias scoring model 226. The bias scoring model 226 may also betrained offline. The bias scoring model 226 takes into account theclustering assignment received from clustering assignment process 220 aswell as some additional features of the new project. The bias scoringmodel 226 provides as output information an output bias risk score 228,a list of bias factors 230 and a list of similar projects 232.

In some embodiments, one or more outputs of the bias scoring model,including the bias risk score 228, the list of bias factors 230 and thelist of similar projects 232, are provided to a recommendation module225. In some embodiments, the recommendation module 225 may also receivethe extracted features of the new project 202 produced by the featureextraction process 210 and the output of the clustering assignmentprocess 220. The recommendation module 225 operates to provide arecommendation to reduce or eliminate the potential bias determined bythe method 200, if any. The recommendation produced by therecommendation module 225 may be based on any available information,including the bias risk score 228, the list of bias factors 230, thelist of similar projects 232, the extracted features, etc.

The nature of the recommendation provided by the recommendation module225 may be of any form suitable for the project 202. For example, therecommendation module 225 may compare the bias risk score 228 with apredetermined threshold and produce a recommendation based on thecomparison. Further, the recommendation module 225 may makerecommendations based on a comparison with a threshold, plus otherinformation about the proposed project 202. For example, if the natureof the proposed project 202 is a new telecommunication service offeringto a targeted geographical area, if the bias risk score 228 exceeds apredetermined geographic threshold, the recommendation module 225 mayrecommend changing geographical parameters of the telecommunicationservice offering. Similarly, if the bias score exceeds a predeterminedincome threshold for income ranges of likely purchasers of the newservice, the recommendation module may recommend changing income relatedfeatures of the telecommunication service offering. Other extensions andvariations may be readily made. In other examples, recommendations mightinclude changing a geographic distribution for an advertising campaignfor the proposed new project or changing a targeted age range forcustomers for the proposed project.

Further, the recommendation may include one or more factors of the listof bias factors 230 to review and adjust in order to eliminate or reducethe risk of potential bias for the proposed new project. Further, therecommendation may include a recommendation to review projects on thelist of similar projects 232 to analyze if such projects includedpreviously undetected bias. The recommendation module 225 may include adashboard or some other user interface 227 for interaction by a humanfor providing the recommendation and receiving input from the human, orfor receiving a selection instruction about a correction to make toproject data for the project 202. For example, the user interface mayprovide the one or more outputs of the bias scoring model to a human forreview and consideration. The human may decide to adjust some aspect ofproject data as a way to mitigate or reduce potential bias detected bythe method 200.

In some embodiments, information about the recommendation may beprovided to the bias correction module 235. The bias correction module235 may operate to modify one or more aspects of the project 202 basedon the recommendation. For example, if the recommendation involveschanging the targeted age range for customers of the project, the biascorrection module 235 may implement that change in the data for theproject stored in the project database 208. The method 200 may then berepeated, automatically or with human intervention, based on thecorrections produced by the bias correction module 235.

The bias risk score 228 represents a quantification of the risk that thenew project 202 has a potential undesirable bias. The entity operatingthe method can determine the relative importance of the value that isproduced. The list of bias factors 230 represents factors thatinfluenced the bias risk score 228. The list of similar projects 232helps a user identify projects that are statistically similar to the newproject 202. This information can provide context for understanding theresults from the bias scoring model 226, such as how this projectrelates to the others and what kinds of potential bias are present inother projects, etc. In addition, this helps the user identify projectsin the past that may have been similar and thereby reduces therepetitive nature of work. Solutions adapted for the earlier, similarproject may be adopted or adapted for the current new project. The listof similar projects 232 may serve as identification of projects that maybe useful in identifying and addressing potential bias in the presentnew project 202.

FIG. 2A illustrates an example. In step 216, a description of a projectis accessed and the process retrieves exemplary text. The text may be anabstract of the project: “Machine bias can arise when machine learningand AI algorithms make erroneous assumptions about the units in apopulation sample, causing the algorithm to draw improper conclusionsabout the population upon which the algorithm will be applied.”Application of the keyword extraction algorithm, step 218, providesexample keywords as follows for this example: bias; machine; model;population; algorithm; class; application; deploy; source; recognition.After determining a clustering assignment by the clustering assignmentprocess 220, and applying the extracted keywords to the clustering model222 and determining which cluster the new project 202 belongs to,project information is provided to the bias scoring model 226. For thisexample, the bias scoring model 226 determines an output bias risk scoreof 0.7, a list of bias factors 230 including the data set used and thedemographic of the sample. Finally, the bias scoring model 226 providesa list of three similar projects 232 that may be consulted, identifiedas Project 23, Project 85 and Project 485.

FIG. 2B depicts an illustrative embodiment of method 237 for trainingand saving the clustering model 222 in accordance with various aspectsdescribed herein. The clustering model 222 operates in conjunction withthe project database 208 and method 237 includes a feature extractionoperation 236, a cluster model training operation 246 and a save modeloperation 248. The project database 208, as discussed in conjunctionwith FIG. 2A, stores information about past and current projects,illustrated in FIG. 2B as project 1 234A, project 2 234B, project 3 234Cthrough project N 234N. Any number of projects may be stored in theproject database 208 and it is expected that the number of projects willincrease over time as further projects are evaluated.

Projects stored in the project database 208 may be actively gathered orcollected from different sources. For example, a project may include awebsite, either internal to the entity or company, or a publiclyavailable website. The website might include a textual description ofthe project or graphics or audio or video files that form the project.The website in some embodiments may include a list of project personnelincluding one or more project managers. The website may include projecttimelines and review materials. The website may include one or morepublications such as white papers with details about one or more aspectsof the project. Further, a website is just one example of a resourceavailable on a network, either a public or private network. Instead ofor in addition to a website, the entity may use collaboration tools suchas GoToMeeting® or Slack® which gather and store information produced byproject contributors. The information may be collected in any suitablemanner, such as by crawling one or more networks to locate and retrievethe information. Any such information, however formatted, may becollected as a project stored in the project database 208.

The feature extraction operation 236 operates to retrieve projects fromthe project database 208 and extract features from each project. In anembodiment, each respective project, project 1 234A, project 2 234B,project 3 234C through project N 234N are individually retrieved fromthe project database 208 and evaluated. The feature extraction operation236 in some embodiments includes a get project description operation238, a get business unit operation 240, and a get project manageroperation 242.

The get project description operation 238 operates to retrieve from aproject such as project 1 234A a textual description of the project. Theget project description operation 238 may employ a keyword extractionfunction 244 to extract features from the textual description of theproject. For example, a standard keyword extraction function such asTextRank or TF-IDF may be used. Alternatively, in other embodiments,other keyword extractors may be used, or other processes may be used toidentify the focus or key points of the project as expressed in thetextual description.

The get business unit operation 240 operates to retrieve and identify abusiness unit associated with a project in the project database 208. Thebusiness unit may be a department or cost center or operating functionor group within an entity. The get project manager operation 242operates to retrieve and identify a project manager associated with aproject in the project database 208. The project manager may beidentified as one or more persons involved with supervising and managingthe planning and progress of the retrieved project.

Other operations may be included as well. Functions such as the getproject description operation 238, the get business unit operation 240,the get project manager operation 242 and others operate to provideinformation about what past or current projects in the project database208 may be similar to or related to a current project of interest. Forexample, if a current project is being developed within the samebusiness unit that developed an earlier project such as project 1 234A,there may be useful similarities that justify further consideration ofproject 1 234A. Similarly, if a current project is being developed underthe same project manager that developed an earlier project such asproject 2 234B, there may be useful similarities that justify furtherconsideration of project 2 234B.

The feature extraction operation 236 operates to identify and retaininformation that is most relevant to the project. This may includedeleting irrelevant data, organizing data into one or more standardformats, and extracting information, such as keywords, from the data ofthe project. If any data is in the form of text, a natural languageprocessing (NLP) function such as the keyword extraction function 244may be used to identify important concepts and features of the project.

The cluster model training operation 246 uses the information extractedfrom the various projects to train the clustering model 222 in FIG. 2A.Model training may be performed in any suitable fashion. Generally, theinformation extracted from the various projects forms training data fortraining the clustering model 222 in. The cluster model trainingoperation 246 implements a learning algorithm that finds patterns in thetraining data that map input data attributes to a target, identify ananswer to be predicted, and generate as outputs an update to theclustering model 222 that captures these patterns. Once the clusteringmodel 222 is trained, the save model operation 248 saves the clusteringmodel 222 in a suitable location.

As indicated above in conjunction with FIG. 2A, the saved clusteringmodel 222 can be used to evaluate new projects such as the new project202. Moreover, as each new project is evaluated, the clustering model222 is updated when the update clustering model process 224 isperformed. This ensures that the clustering model 222 is always usingthe latest version of the dataset forming the clustering model 222. Whena new project is received, features are extracted and provided to theclustering model 222. An output from the clustering model 222 is anindication of what cluster the new project should belong to, based onthe existing trained clustering model 222. The clustering modelimplements one or more distance metrics to develop a conclusion aboutwhere to assign newly processed projects. As the clustering model 222 isupdated, previous clustering assignments for previous projects may bechanged based on the updated model. Moreover, new clusters may beidentified or designated, and previously processed clusters can be movedto a new cluster. Clusters are thus automatically learned and re-learnedfrom the on-going processing of new projects such as new project 202.

The list of similar projects 232 produced by the bias scoring model 226thus provides a substantial technical benefit of improving accuracy onan on-going basis. A new project comes in and based on the foregoingembodiments illustrated by FIG. 2A may show the existence of a potentialbias based on a comparison to a bias threshold. Alternatively, the biasscoring model 226 can provide a raw bias score for the new project thatcan be compared to bias scores of other similar projects enabling usersto make their own determination as to whether bias is present. In eitherembodiment, the list of similar projects 232 can serve as a reference todetect a potential for bias in the new project.

The learning process represented by the feedback loop including theupdate clustering model process 224 provides substantial technicalbenefits. It improves the accuracy and reliability of the clusteringmodel 222. Consequently, when new projects get added to the projectdatabase 208, the ability to detect potential bias increases in accuracyand reliability.

FIG. 2C depicts an illustrative embodiment of a method 250 in accordancewith various aspects described herein. In particular, FIG. 2Cillustrates a method for training and saving the scoring model 226 ofFIG. 2A. The method 250 begins with the trained clustering model 222 andthe project database 208 of projects. The method 250 includes a clusterextraction operation 252, a similarity calculation operation 254, aproject feature extraction operation 256, a labelling operation 258,bias scoring model training operation 260 to train the bias scoringmodel 226, shown in FIG. 2A, and to train the results via a save modeloperation 262. More, different or fewer operations may be included inalternative embodiments. Initially, the bias scoring model 226, FIG. 2A,can be trained, and, in some instances, this includes manual stepsperformed via human intervention or initialization.

As noted earlier, clustering model 222 is trained to identify that aproject belongs to one or more clusters. The definition and contents ofthe clusters are repeatedly updated as new projects are evaluated. Atthe cluster extraction operation 252, the trained clusters are extractedfrom the clustering model 222. Initially, all clusters are extractedfrom the clustering model 222.

In an exemplary embodiment, the similarity calculation operation 254calculates the similarity of each project within each cluster suppliedby the clustering model 222. For example, if there are five clusters inthe clustering model 222, the similarity calculation operation 254 firstlooks at all projects within a first cluster and determines how similarall projects in the first cluster are to each other. Then, thesimilarity calculation operation 254 next looks at all projects within asecond cluster and determines how similar all projects in the secondcluster are to each other. This continues for all clusters in thecluster model, up through the fifth cluster in this example.

Similarity among projects may be determined in any suitable manner. Oneexample is cosine similarity in which each project is treated as avector through an appropriate embedding. Similarity between projects isdetermined by an angle between vectors of corresponding projects. Thesmaller the angle, the more similar the projects are. Other techniquesfor similarity determination may be readily substituted.

The project database 208 stores information about all projects. Theproject feature extraction operation 256 operates to extract allfeatures of a project, as discussed earlier. Other feature extractionprocesses may be used as well.

Further, the labelling operation 258 operates to assign labels toprojects in the database that are believed or determined to have a riskof potential bias therein. For the initial training for the bias scoringmodel 226, there must be information about projects that are known toexhibit a risk of bias. For example, all projects in the database 208may be reviewed and assigned a descriptor or value, which can indicateif the project exhibits bias or a risk of bias, or if the project doesnot exhibit potential bias or a risk of bias. In some embodiments, thisinitial labelling operation is done by a human. In some applications, anautomated process may be substituted.

The bias scoring model training operation 260 receives from thesimilarity calculation operation 254 information about projectsimilarity and project cluster assignments, the extracted projectfeatures from the project feature extraction operation 256 and theproject labels from the labelling operation 258. This information isused by the bias scoring model training operation 260 to train a newbias scoring model 226. The new bias scoring model 226 is saved by themodel saving operation 262 and may be saved in any convenient locationusing any suitable format. As noted earlier, the bias scoring model 226is a machine learning model useful for detecting potential bias in newprojects based on experience with earlier projects. The bias scoringmodel 226 is automatically updated as each new project is processed.That is, as the bias scoring model 226 determines that a new projectexhibited bias, or did not exhibit bias, that result is fed back intothe model to improve the accuracy and reliability of the bias scoringmodel 226. Example models that may be adapted to form the bias scoringmodel 226 include logistical regression models or random forest modelsor a deep learning approach. Selection of a model may depend on factorssuch as the nature of the dataset, the size of the dataset and desiredoutput of the model.

FIG. 2D depicts an illustrative embodiment of a method 264 in accordancewith various aspects described herein. The embodiment of FIG. 2D can beused in place of, or in conjunction with, the steps of the embodiment ofFIG. 2A for identifying potential bias in a project using informationabout previous projects. In particular, method 264 illustrates anembodiment of a method for populating a machine learning (ML) database(MLDB) 274 in a system for identifying potential bias in a project.

The method 264 for populating the machine learning database MLDB 274includes a step 265 of extracting features from one or more projects202, a step 268 of applying feature weights, a step 270 of clusteringthe projects and a step 272 of assigning bias scores to clusters.Information about projects is added to the machine learning databaseMLDB 274.

The projects 202 generally contain data and other information about pastor current projects of an entity, business organization, department orotherwise. The projects 202 may be completed projects, projects that arenow under development, projects that have been completed. The projects202 may originate within an entity or organization or may originateexternal to the organization. At least some of the projects 202 mayillustrate some potential bias, its operation or its results. Eachproject 202 may have project information for the project 202 stored in alocation such as a project database 266.

A project 202 may be defined to include a network location defined by awebsite or uniform resource locator and be either internal or externalto the entity or company. In this example, the website in someembodiments includes information for the project 202 such as a textualdescription of the project 202, graphics or audio or video files, orother data that form the project 202. The website might includeadditional information such as a list of project personnel including oneor more project managers. Project information for the project 202 mayinclude project timelines and review materials. Project information forthe project 202 may include copies or drafts of one or more publicationssuch as white papers with details about one or more aspects of theproject. Further, a website is just one example of a resource availableon a network that is either a public or private network. Instead of orin addition to a website, the entity may use collaborations tools forteam members. The collaboration tools may gather and store informationproduced by project contributors. The project information may becollected in any suitable manner, such as by crawling one or morenetworks to locate and retrieve the project information. Any suchinformation, however formatted, may be collected as a project stored inthe project database 266.

The step 265 of extracting features from a project 202 may includeaccessing project data stored in the project database 266 and any otheravailable sources of project information. The step 265 of extractingfeatures from the project 202 may include extracting any information ordata that may be useful for determining the presence or potential forbias in the project 202. For example, in some embodiments, the step 265of extracting features from a project 202 may include extracting aproject description 276 and extracting project metadata 278 for theproject 202.

The project description 276 may be a textual description of the project202. In some embodiments, the project description 276 may be at a highlevel, including just a few descriptive sentences. In other embodiments,the description 276 may include detailed information about the project202.

One embodiment of step 265 of extracting features from a project 202 mayinclude a step 280 of extracting or generating keywords from the projectdescription 276. Extracting keywords from short texts can be achievedvia co-occurrence models. These models not only consider the individualwords that are present in the text, but also examine how likely twowords are to appear together. This provides necessary context and weightto relevant words. One such co-occurrence model is TextRank, which isbased on the PageRank algorithm of Google®. With TextRank, a window isdefined in which to examine co-occurring words. Words are denoted asnodes and an undirected edge is defined as a link between two nodes inthe window. The TextRank algorithm seeks to learn the weights of theedges between nodes. These weights provide information about thestrength of the co-occurrence. The edges with the largest weightscorrespond to the nodes that are most important, which can serve toidentify the keywords of the text.

To illustrate the effectiveness of TextRank, the following are a fewexamples where keywords are generated using this algorithm. In a firstexample, a project abstract states as follows: “To support the company'snew advertising division, our team is developing checks that could helpto ensure that the targeted audience for an advertising campaign is notaccidently biased towards certain protected or sensitive groups ofconsumers. This approach focuses on using possible disparate impact tomeasure potential improper discrimination in targeted advertising, andproposes an approach based on machine learning techniques that allow usto infer the relevant audience for a campaign from an initial targetedlist provided by an advertiser. The inferred relevant audience can thenbe used to test for potential bias across different consumer groups. Ourmethods can also be used to expand the targeted audience in a way thatwill help mitigate any detected potential bias. These proactive stepssupport the company's long-term commitment to the responsible use ofcustomer information as well as maintaining a brand-safe environment forour advertising clients.”

Keywords detected for this passage include the following: advertise;audience; bias; target; consumer; infer; group; campaign; support;inform.

In a second example, a Project Abstract states as follows: “Machine biascan arise when machine learning and AI algorithms make erroneousassumptions about the units in a population sample, causing thealgorithm to draw improper conclusions about the population upon whichthe algorithm will be applied. These types of errors could lead to thedeployment of highly accurate models that are perceived to be unfair tosegments of the target population. There have been a number ofhigh-profile examples in which model-based technologies such as imagecaptioning, speech recognition, and facial recognition reportdifferential performance for members of the majority and minorityclasses when studied across racial/ethnic groups, genders, and otherprotected classes. In this talk, I will provide a working definition ofmachine bias and explain why it is important for data scientists to takeit into consideration when building and deploying models. I willidentify several key sources of machine bias and propose methods thatcould be applied to mitigate them. Finally, I will highlight a few opensource tools that could be potentially helpful in bias detection andcorrection.”

Keywords detected for this passage include the following: bias; machine;model; population; algorithm; class; application; deploy; source;recognition.

In a third example, a Project Abstract states as follows: “Networks,also known as graphs, are one of the most crucial data structures in ourincreasingly intertwined world. Social friendship networks, theworld-wide web, financial systems, infrastructure (power grid, streets),etc. are all network structures. Knowing how to analyze the underlyingnetwork topology of interconnected systems can provide an invaluableskill in anyone's toolbox. This presentation will provide a hands-onguide on how to approach a network analysis project from scratch andend-to-end: how to generate, manipulate, analyze and visualize graphstructures that will help you gain insight about relationships betweenelements in your data.”

Keywords detected for this passage include the following: network;structure; provide; analyze; system; world; graph; datum.

Other examples of keyword detection may be readily imagined. Any othersuitable algorithm, process or tool may be used to perform keyworddetection. Once keywords are extracted, other less relevant words arefiltered out. The result may be provided to a clustering assignmentprocess.

In addition to project descriptions, projects such as project 202 mayalso have metadata such as metadata 278 associated with them. Suchmetadata 278 may include, for example, datasets used, contributors, endgoals, business units, project personnel, etc. This metadata 278 may begrouped with the keywords that are extracted from the projectdescriptions 276 as part of the step 280 of extracting or generatingkeywords from the project description 276. Such a grouping creates afeature representation for the project 202.

As noted above, a plurality of projects 202 is processed to extractfeature data in step 265 from the projects 202. Repeating theseprocesses of keyword extraction, step 280, and mining of metadata, step278 for many projects 202 will give feature representations for adiverse set of ML projects 202. Some embodiments also introduce a step268 of applying feature weights to one or more of the projects 202.Applying feature weights in step 268 creates a weighting scheme thatgives relative importance to risk factors believed to have moreinfluence on bias. Examples include existence of project keywords thatmay carry more weight than association with certain businessorganizations. Selecting and applying feature weights may be done on anysuitable basis including experience with the types of projectinformation and types of potential bias previously detected.

Once features have been extracted from the projects 202 at step 265, astep 270 of clustering the projects can be performed based on theextracted features and taking into consideration the feature weights.Projects which contain a potential for bias will have a shared set offeatures and will be clustered in similar groups.

Finally, in a step 272 of assigning bias scores to clusters, eachcluster is assigned a preliminary bias score. The clusters with theirrespective bias scores define an ontology of bias or clustering model.The clustering model may be stored in the database MLDB 274. Thedatabase MLDB 274 is thus initialized or built based on pre-existingprojects 202 and information about those projects.

Subsequently, when a new project is added to the database MLDB 274, itskeywords and metadata may be extracted to create a featurerepresentation. Features of the new project are then fed through theontology to determine which clusters the new project belongs to. Anoverall bias score is determined for the new project and acategorization is assigned, such as low, medium, or high, on thepotential for bias in the new project.

As new projects are added to the database MLDB 274, not only are theirrespective bias scores and categorizations determined, but thebias-ontology itself may also be retrained in order to update the biasscore for each cluster. This produces a continuous learning mechanism sothat the bias-ontology adapts to changing projects, terminology, data,and other risk factors. The retraining and project scoring can becomputed offline. This provides flexibility to assess which risk factorswill continue to be used and what feature weighting scheme will beemployed, such as in step 268. Factors that influence potential bias maychange over time and maintaining the offline nature of building theontology allows for careful consideration of potential risk factors soas not to skew the clustering. Thus, a substantial technologicalimprovement is provided in that the bias-ontology is updated on anon-going basis to ensure accurate clustering and bias scoredetermination.

Populating the machine learning database MLDB 274, creation of thebias-ontology, and continuous learning are all performed offline.Therefore, computational and temporal efficiency are not an issue. Theoutput of a bias categorization system such as described by method 264illustrated in FIG. 2D provides the project manager with the authorityto assess their own risk tolerance of when concern is warranted.

The foregoing systems and methods as illustrated by FIGS. 2A-2Dfacilitate identification of a potential bias in a project as early aspossible in the machine learning lifecycle of a new project. Also, thesystem helps to identify other projects, that may or may not be relatedto a new project, that exhibit similar or related bias.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2A-2D,it is to be understood and appreciated that the claimed subject matteris not limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 3, a block diagram of a communication network 300is shown illustrating an example, non-limiting embodiment of avirtualized communication network 300 in accordance with various aspectsdescribed herein. In particular the virtualized communication network300 can be used to implement some or all of the subsystems and functionsof communication network 100, the subsystems and functions of method200, method 237, method 250 and method 264 presented in FIGS. 1, 2A, 2B,2C, and 3. For example, virtualized communication network 300 canfacilitate in whole or in part determining a potential risk forintroduction of potential bias in a machine learning project, usingmachine learning models that cluster projects and score the machinelearning projects for potential bias.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic, so that the resources are only consumed when needed. In asimilar fashion, other network elements such as other routers, switches,edge caches, and middle boxes are instantiated from the common resourcepool. Such sharing of infrastructure across a broad set of uses makesplanning and growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized and might require special DSP code andanalog front ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud or might simply orchestrateworkloads supported entirely in NFV infrastructure from thesethird-party locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment 400 in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part determining a potential risk forintroduction of potential bias in a machine learning project, usingmachine learning models to cluster projects and score the machinelearning projects for potential bias.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM),flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system 430, application programs 432, program modules 434,and/or program data 436 can also be cached in the RAM 412. The systemsand methods described herein can be implemented utilizing variouscommercially available operating systems or combinations of operatingsystems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part determining a potential risk for introduction ofpotential bias in a machine learning project, using machine learningmodels that cluster projects and score the machine learning projects forpotential bias. In one or more embodiments, the mobile network platform510 can generate and receive signals transmitted and received by basestations or access points such as base station or access point 122.Generally, mobile network platform 510 can comprise components, e.g.,nodes, gateways, interfaces, servers, or disparate platforms, thatfacilitate both packet-switched (PS) (e.g., internet protocol (IP),frame relay, asynchronous transfer mode (ATM)) and circuit-switched (CS)traffic (e.g., voice and data), as well as control generation fornetworked wireless telecommunication. As a non-limiting example, mobilenetwork platform 510 can be included in telecommunications carriernetworks and can be considered carrier-side components as discussedelsewhere herein. Mobile network platform 510 comprises CS gatewaynode(s) 512 which can interface CS traffic received from legacy networkslike telephony network(s) 540 (e.g., public switched telephone network(PSTN), or public land mobile network (PLMN)) or a signaling system #7(SS7) network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology ortechnologies utilized by mobile network platform 510 fortelecommunication over a radio access network 520 with other devices,such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer or computers, those skilled in the art will recognize that thedisclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,communication device 600 can facilitate in whole or in part determininga potential risk for introduction of bias in a machine learning project,for example using machine learning models to cluster projects and scorethe machine learning projects for potential bias.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad as thekeypad 608 with a navigation mechanism such as a roller ball, ajoystick, a mouse, or a navigation disk for manipulating operations ofthe communication device 600. The keypad 608 can be an integral part ofa housing assembly of the communication device 600 or an independentdevice operably coupled thereto by a tethered wireline interface (suchas a USB cable) or a wireless interface supporting for exampleBluetooth®. The keypad 608 can represent a numeric keypad commonly usedby phones, and/or a QWERTY keypad with alphanumeric keys. The UI 604 canfurther include a display 610 such as monochrome or color LCD (LiquidCrystal Display), OLED (Organic Light Emitting Diode) or other suitabledisplay technology for conveying images to an end user of thecommunication device 600. In an embodiment where the display 610 istouch-sensitive, a portion or all of the keypad 608 can be presented byway of the display 610 with navigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high-volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ machinelearning to facilitate automating one or more features described herein.The embodiments (e.g., in connection with automatically identifyingacquired cell sites that provide a maximum value/benefit after additionto an existing communication network) can employ various ML-basedschemes for carrying out various embodiments thereof. Moreover, theclassifier can be employed to determine a ranking or priority of eachcell site of the acquired network. A classifier is a function that mapsan input attribute vector, x=(x1, x2, x3, x4, . . . , xn), to aconfidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/orstatistically-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to separate the triggering criteria from thenon-triggering ones. Intuitively, this makes the classification correctfor testing data that is near, but not identical to training data. Otherdirected and undirected model classification approaches comprise, e.g.,naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzylogic models, and probabilistic classification models providingdifferent patterns of independence can be employed. Classification asused herein also is inclusive of statistical regression that is utilizedto develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the words “example” or “exemplary” is intendedto present concepts in a concrete fashion. As used in this application,the term “or” is intended to mean an inclusive “or” rather than anexclusive “or”. That is, unless specified otherwise or clear fromcontext, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: receiving project data defining aproposed project of an entity; storing in a project database, theproject data, wherein the storing comprises storing the project datawith other project data for other projects; extracting from the projectdata features of the proposed project; based on the extracted features,determining a clustering assignment for the proposed project, whereinthe determining the clustering assignment comprises comparinginformation about the proposed project, including the extractedfeatures, with information about the other projects and assigning theproposed project to a cluster, the cluster including one or moreprojects of the other projects having similar bias characteristics asthe proposed project; determining a risk of bias for the proposedproject; and based on the risk of bias, recommending a corrective actionto reduce the risk of bias.
 2. The device of claim 1, wherein theoperations further comprise: providing the extracted features of theproposed project to a clustering model and receiving, from theclustering model, the clustering assignment for the proposed project;and updating the clustering model based on the extracted features of theproposed project.
 3. The device of claim 2, wherein the updating theclustering model comprises: updating clustering assignments for theother projects based on the extracted features of the proposed project.4. The device of claim 2, wherein the operations further comprise:receiving project information for a plurality of existing projects;training the clustering model using the project information, wherein thetraining of the clustering model comprises implementing a machinelearning algorithm that finds patterns in the project information andproduces the clustering model; and assigning each respective project ofthe plurality of existing projects to a respective cluster of aplurality of clusters of the clustering model.
 5. The device of claim 4,wherein the updating the clustering model comprises: subsequentlyupdating clustering assignments for the each respective project of theplurality of existing projects based on the extracted features of theproposed project.
 6. The device of claim 1, wherein the operationsfurther comprise: determining a list of bias factors for the proposedproject, wherein the determining the list of bias factors comprisesdetermining one or more factors that influenced the risk of bias for theproposed project.
 7. The device of claim 1, wherein the operationsfurther comprise: receiving a project description and metadata for theproposed project; extracting keywords from the project description;determining from the metadata for the proposed project, project dataincluding one or more of a business unit for the proposed project, aproject manager for the proposed project and project personnel for theproposed project; and determining the extracted features of the proposedproject, wherein the determining the extracted features is based on thekeywords and the project data.
 8. The device of claim 1, wherein theoperations further comprise: extracting cluster features of a pluralityof clusters of a clustering model, wherein the extracting the clusterfeatures comprises receiving from the clustering model the features ofall projects assigned to respective clusters of the clusters of theclustering model; determining project similarity for each respectivecluster of the clusters of the clustering model, wherein the determiningthe project similarity comprises determining a similarity of allrespective projects within the respective cluster; receiving labellinginformation identifying one or more projects of the other projects knownto have a risk of bias; and based on the extracted cluster features, theproject similarity for the each respective cluster and the labellinginformation, training a bias scoring model, wherein the training thebias scoring model comprises, implementing a machine learning algorithmthat finds patterns in the extracted cluster features, the projectsimilarity for the each respective cluster and the labelling informationand produces the bias scoring model.
 9. The device of claim 8, whereinthe determining a risk of bias for the proposed project comprises:providing the clustering assignment for the proposed project to the biasscoring model; and receiving the risk of bias for the proposed project.10. The device of claim 1, wherein the proposed project and the otherprojects comprise machine learning (ML) projects.
 11. A method,comprising: receiving, by a processing system including a processor,proposed project data for a proposed project, wherein the receivingproposed project data comprises receiving machine learning (ML) data,and wherein the proposed project is an ML project; storing, by theprocessing system, the proposed project data in a project database whichstores other project data for a plurality of other projects, whereinrespective other projects of the plurality of other projects are MLprojects; extracting, by the processing system, features and metadata ofthe proposed project data; providing, by the processing system, thefeatures and metadata of the proposed project data to a clusteringmodel, wherein the clustering model comprises an ML model; receiving, bythe processing system, a clustering assignment for the proposed projectfrom the clustering model; providing, by the processing system, theclustering assignment and the features of the proposed project and themetadata of the proposed project to a bias scoring model, wherein thebias scoring model comprises an ML model; receiving, by the processingsystem, from the bias scoring model, an indication of risk of potentialbias for the proposed project; providing, by the processing system, arecommendation for reducing a risk of potential bias for the proposedproject, wherein the providing the recommendation for reducing the riskof potential bias comprises one or more of: providing, by the processingsystem, a bias risk score; providing, by the processing system, a listof bias factors affecting the bias risk score, wherein respective biasfactors of the list of bias factors may be adjusted to reduce the riskof potential bias for the proposed project; and providing, by theprocessing system, a list of one or more projects of the plurality ofother projects having similar bias characteristics as the proposedproject.
 12. The method of claim 11, further comprising: determining, bythe processing system, the clustering assignment for the proposedproject to a cluster of a plurality of clusters, wherein each otherproject of the plurality of other projects is assigned to a respectivecluster of the plurality of clusters, and wherein the determining theclustering assignment is based on the features and metadata of theproposed project data.
 13. The method of claim 11, further comprising:training, by the processing system, the clustering model, wherein thetraining the cluster model comprises: retrieving, by the processingsystem, stored other project data of the plurality of other projects;extracting, by the processing system, features of the plurality of otherprojects and metadata from the other project data of the other projects;and applying the extracted features of the proposed project data and themetadata of the proposed project data, and the extracted features of theother projects and the metadata of the other project to a machinelearning (ML) model.
 14. The method of claim 11, further comprising:training, by the processing system, the bias scoring model, wherein thetraining the bias scoring model comprises: retrieving, by the processingsystem, project data for the plurality of other projects from theproject database; for each respective cluster of a plurality of clustersof projects, determining a respective similarity value representingsimilarity of projects associated with the each respective cluster;extracting other project features of the other projects in the projectdatabase; receiving label assignments for at least some projects of theother projects, wherein the label assignments indicate that the at leastsome projects have an associated risk of bias; and providing therespective similarity values, the other project features and the labelassignments to a machine learning (ML) model.
 15. A machine-readablemedium, comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations, the operations comprising: receiving project data defining aproposed project of an entity; storing the project data in a projectdatabase, wherein the storing comprises storing the project data withother project data for other projects; extracting features of theproposed project from the project data, wherein the extracting comprisesextracting keywords from a textual project description of the proposedproject; retrieving metadata of the proposed project; determining aclustering assignment for the proposed project, wherein the determiningcomprises applying at least some of the extracted features of theproposed project and at least some of the metadata of the proposedproject to a clustering model to identify a cluster of a plurality ofclusters of projects, wherein the identified cluster includes projectssimilar to the proposed project; providing the clustering assignment andat least some of the extracted features of the proposed project and atleast some of the metadata of the proposed project to a bias scoringmodel; receiving from the bias scoring model an indication of risk ofpotential bias for the proposed project; based on the indication of riskof potential bias, providing a recommendation for reducing the risk ofpotential bias for the proposed project, wherein the providing arecommendation for reducing the risk of potential bias comprises:providing a bias risk score; and providing a list of bias factorsaffecting the bias risk score, wherein respective bias factors of thelist of bias factors may be adjusted to reduce the risk of potentialbias for the proposed project.
 16. The machine-readable medium of claim15, wherein the providing the recommendation for reducing the risk ofpotential bias further comprises: providing a list of similar projects,wherein respective projects of the list of similar projects may beevaluated to determine how a risk of potential bias for a respectiveproject of the list of similar projects was reduced.
 17. Themachine-readable medium of claim 15, wherein the operations furthercomprise: training the clustering model using project data from theother projects stored in the project database.
 18. The machine-readablemedium of claim 17, wherein the training the clustering model comprises:retrieving, from the project database, the project data from the otherprojects, wherein the retrieving the project data comprises retrievingextracted features of the other projects and metadata of the otherprojects; and applying the extracted features of the proposed projectand the metadata of the proposed project, and the extracted features ofthe other projects and the metadata of the other project to a machinelearning (ML) model.
 19. The machine-readable medium of claim 18,wherein the operations further comprise: receiving subsequent projectdata defining a subsequent proposed project of the entity; determining aclustering assignment for the subsequent project using the ML model; andupdating the ML model, wherein the updating the ML model comprisesassigning, based on the subsequent project data, the subsequent proposedproject to one cluster of the plurality of clusters of projects andreassigning, based on the subsequent project data, one or more projectsof the other projects to different clusters of the plurality of clustersof projects.
 20. The machine-readable medium of claim 15, wherein theoperations further comprise: training the bias scoring model, whereinthe training comprises: for each respective cluster of the plurality ofclusters of projects, determining a respective similarity valuerepresenting similarity of projects associated with the each respectivecluster; extracting other project features of the other projects in theproject database; receiving label assignments for at least some projectsof the other projects, wherein the label assignments indicate that theat least some projects have an associated risk of bias; and providingthe respective similarity values, the other project features and thelabel assignments to a machine learning (ML) model.